Computer and Computing Program

ABSTRACT

Provided is a computer that does not need quantum coherence or a cryogenic cooling device for a problem to be solved that needs an exhaustive search and a computing program thereof. Spin sj as a variable is caused to follow a local effective magnetic field Bj to introduce a system to a ground state of a problem-setting system. The ground state is a solution. At t=0, the magnetic field Bj is applied in the x-axis direction at all sites and all spins sj are initialized to the x-axis direction. With the lapse of time t, a magnetic field in the z-axis direction and interspin interaction are gradually applied, spin becomes +z-direction or −z-direction finally, and the z-component of spin sj becomes sjz=+1 or −1. When the direction spin sj is caused to follow the direction of the effective magnetic field Bj, a relaxation term to keep the direction of spin sj is introduced to improve convergence of a solution.

TECHNICAL FIELD

The present invention relates to a computer which enables a high-speed computation for an inverse problem or a combinatorial optimization problem requiring an exhaustive search.

BACKGROUND ART

As represented by words such as big data, the present age is full of data. In information science, knowing how to analyze huge data and how to handle becomes one of the most important problems to be solved. Big data has many problems that need a complex analysis. For example, when a certain result is obtained, it may be desired to find cause of the result. This is referred to as an inverse problem. It becomes difficult to find the cause as a phenomenon becomes more complicated, and in general, efficient algorithm for obtaining an initial value from a result is not present. In the worst case, the exhaustive search should be conducted for the initial value. This is one of the difficult problems in big data. Alternatively, there are also many problems to select an optimal solution from among many choices on the basis of big data. Also, in this case, when all possibilities are taken into account, a need for the exhaustive search comes out. From this background, a computer which efficiently solves a problem which needs the exhaustive search is needed.

On the exhaustive search problem, expectations for a quantum computer are large. The quantum computer simultaneously realizes “0” and “1”, each of which is composed of a basic element called a quantum bit. For that reason, the quantum computer has a potential to simultaneously calculate all solution candidates as the initial value and certainly realize the exhaustive search. However, the quantum computer needs to maintain quantum coherence over the entire calculation time and there appears no prospect that this is realized.

In this situation, a method that has come to be noted is called adiabatic quantum computing (NPL 1). This method is one in which a problem is converted such that a ground state of a certain physical system becomes a solution and the solution is obtained by finding the ground state. The Hamiltonian of a physical system for which a problem is set is assumed as Ĥ_(p). However, the Hamiltonian is not assumed as Ĥ_(p) at a time point of starting computation and is assumed as another Hamiltonian Ĥ₀ with which a ground state is prepared easily and clearly, apart from Ĥ_(D). Next, the Hamiltonian is allowed to transition from Ĥ₀ to Ĥ_(p) by spending enough time. When enough time is spent, a system remains in the ground state and a ground state of the Hamiltonian Ĥ_(p) is obtained. This is the principle of adiabatic quantum computing. When a calculation time is assumed as τ, the Hamiltonian becomes Equation (1).

$\begin{matrix} {{\hat{H}(t)} = {{\left( {1 - \frac{t}{\tau}} \right){\hat{H}}_{0}} + {\frac{t}{\tau}{\hat{H}}_{p}}}} & \left\lbrack {{Equation}\mspace{14mu} 1} \right\rbrack \end{matrix}$

The solution which is time-evolved is obtained based on the Schrodinger equation of Equation (2).

$\begin{matrix} {{i\; \hslash \frac{\partial\;}{\partial t}{{\psi (t)}\rangle}} = {{\hat{H}(t)}{{\psi (t)}\rangle}}} & \left\lbrack {{Equation}\mspace{14mu} 2} \right\rbrack \end{matrix}$

The adiabatic quantum computing is also applicable to the problem that needs the exhaustive search and reaches the solution in a unidirectional process. However, when a calculation process needs to follow the Schrodinger equation of Equation (2), it is necessary to maintain quantum coherence similar to the quantum computer. However, the quantum computer repeats a gate operation for 1 quantum bit or between 2 quantum bits, whereas adiabatic quantum computing is for simultaneously interacting over the entirety of a quantum bit system and the way of thinking for coherence is different. For example, the gate operation to a certain quantum bit is considered. At this time, if there is interaction between the quantum bit and other quantum bits, interaction is cause of decoherence, but in adiabatic quantum computing, all quantum bits are allowed to simultaneously interact and thus, decoherence is not caused in a case such as this example. Adiabatic quantum computing in which this difference is reflected is thought to be robust to decoherence as compared with the quantum computer.

However, there is also a problem to be solved in adiabatic quantum computing. Even though adiabatic quantum computing becomes more robust with respect to decoherence compared to the quantum computer, if a computation process follows the Schrodinger equation of Equation (2), sufficient coherence is needed as well. Matters that a system implement adiabatic quantum computing is a superconducting magnetic flux quantum bit system are a problem to be solved (PTL 1 and NPL 2). This is because in a case of using superconductivity, a cryogenic cooling device is needed. Matters that an extremely low temperature is needed are the problem to be solved for realizing a practical computer.

CITATION LIST Patent Literature

-   PTL 1: JP-T-2009-524857

Non Patent Literature

-   NPL 1: E. Farhi, et al., “A quantum adiabatic evolution algorithm     applied to random instances of an NP-complete problem,” Science 292,     472 (2001). -   NPL 2: A. P.-Ortiz, “Finding low-energy conformations of lattice     protein models by quantum annealing,” Scientific Reports 2, 571     (2012). -   NPL 3: F. Barahona, “On the computational complexity of Ising spin     glass models,” J. Phys. A: Math. Gen. 15, 3241 (1982).

SUMMARY OF INVENTION Technical Problem

As described above, adiabatic quantum computing is effective against a challenge that needs an exhaustive search. However, quantum coherence is still needed and a cryogenic cooling device is also needed in a case of using a superconducting quantum bit. A problem to be solved is to provide a practical computer while eliminating these two necessary conditions.

In order to solve the above-described problem, an object of the present invention is to provide a computer does not need quantum coherence or a cryogenic cooling device and a computing program thereof.

Solution to Problem

Spin is used as a variable in the computation and a problem intended to be solved is set using interspin interaction and a local field acting on each spin. All spins are caused to orient toward one direction by an external magnetic field at time t=0 and the external magnetic field is gradually reduced such that the external magnetic field becomes zero at time t=τ. Each spin is time-evolved in such a way that the direction, which follows an effective magnetic field determined by all actions of interspin interaction and external magnetic fields of each site at time t, is determined. In this case, the direction of spin is not completely aligned in the effective magnetic field and is caused to be a quantum mechanically corrected direction such that the system is caused to maintain in an approximately ground state.

Additionally, a term (relaxation term) to maintain each spin in an original direction during time evolution is added to the effective magnetic field to improve convergence of a solution.

Advantageous Effects of Invention

Although quantum mechanical correction is made in this method, the method operates in a classical system. For that reason, there is no need to take quantum coherence into account and resources in a wide range are available. When energy scale on the bit is set to be sufficiently larger than energy scale of a temperature, temperature fluctuations can also be ignored and a special apparatus such as a cryogenic apparatus and a special environment is not needed.

The relaxation term is added to thereby suppress vibration related to a spin direction in time evolution and improve convergence of a solution.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic diagram for explaining algorithm of the present invention in principle.

FIG. 2 is a diagram illustrating an example of algorithm according to Example 1 as a flowchart.

FIG. 3 is a diagram illustrating an example of algorithm according to Example 2 as a flowchart.

FIG. 4A is a diagram illustrating an example of algorithm according to Example 3 as a flowchart.

FIG. 4B is a diagram illustrating another example of algorithm according to Example 3 as a flowchart.

FIG. 4C is a diagram illustrating another example of algorithm according to Example 3 as a flowchart.

FIG. 4D is a diagram illustrating another example of algorithm according to Example 3 as a flowchart.

FIG. 5A is a diagram illustrating an example of algorithm according to Example 4 as a flowchart.

FIG. 5B is a diagram illustrating another example of algorithm according to Example 4 as a flowchart.

FIG. 5C is a diagram illustrating another example of algorithm according to Example 4 as a flowchart.

FIG. 5D is a diagram illustrating another example of algorithm according to Example 4 as a flowchart.

FIG. 6A is a diagram illustrating an example of algorithm related to a final solution determination method according to Example 5 as a flowchart.

FIG. 6B is a diagram illustrating another example of algorithm related to the final solution determination method according to Example 5 as a flowchart.

FIG. 6C is a diagram illustrating another example of algorithm related to the final solution determination method according to Example 5 as a flowchart.

FIG. 6D is a diagram illustrating another example of algorithm related to the final solution determination method according to Example 5 as a flowchart.

FIG. 7 is a block diagram illustrating a configuration example of a computer, according to Example 8.

FIG. 8 is a block diagram illustrating another configuration example of the computer, according to Example 8.

FIG. 9 is a configuration diagram illustrating a configuration example of a local field response computing device included in a computer, according to Example 9.

FIG. 10 is a configuration diagram illustrating a configuration example of an optical part of FIG. 9.

FIG. 11 is a block diagram illustrating a configuration example of a local field response computing device included in a computer, according to Example 10.

FIG. 12 is a block diagram illustrating a configuration example of a local field response computing device included in a computer, according to Example 11.

FIG. 13 is a block diagram illustrating a configuration example of a local field response computing device included in a computer, according to Example 12.

DESCRIPTION OF EMBODIMENTS

In the following, various examples of the present invention together with a principle of computation will be described with reference to the accompanying drawings. However, the present invention is not to be construed as being limited to the description in the following embodiment. Matters that change to a specific configuration of the present invention may be made without departing from the spirit or gist of the present invention is easily understood by a person skilled in the art.

In a configuration of an invention to be described below, the same portions or portions having similar functions are denoted by the same reference numerals to be commonly used in different drawings and redundant descriptions thereof will be omitted.

Notations such as “first”, “second”, and “third” in the present specification are intended to identify constituents and are not necessarily intended to limit the number or order. Furthermore, the number for identifying constituents is used for each context and the number used in one context does not necessarily indicate the same configuration also in other contexts. The constituent identified by a certain number is not precluded from functioning as the constituent identified by other numbers as well.

A position, size, shape, range or the like of each configuration indicated in the drawings and the like may not represent an actual position, size, shape, range or the like for easy understanding of an invention. For that reason, the present invention is not necessarily limited to the position, size, shape, range or the like disclosed in the drawings and the like.

Adiabatic quantum computing is also called quantum annealing as an alias and is one obtained by developing the concept of classical annealing to quantum mechanics. That is, adiabatic quantum computing can operate in a classical basis of behavior and can be interpreted that quantum mechanical effects are added in order to improve performance in terms of high-speed or a correct answer rate of a solution. In the present invention, a computing component itself is assumed to classical one and parameters determined quantum mechanically are introduced in the computation process to thereby realize a computing method and apparatus that are classical ones but include quantum mechanical effects.

Based on the concept described above, a classical algorithm to obtain a ground state as a solution and an apparatus for realizing will be described in the following description, while explaining relevance to adiabatic quantum computing.

A typical form to be described in the following example is a computer which includes a computing unit, a storing unit, and a control unit and performs computation while exchanging data between the storing unit and the computing unit by control of the control unit, and in which N variables s_(j) ^(z) (j=1, 2, . . . , N) take a range of −1≦s_(j) ^(z)≦1 and a problem to be solved is set using a local field g_(j) and intervariable interaction J_(ij) (i, j=1, 2, . . . , N). In the computing unit, computation is discretely performed from t=t₀ (t₀=0) to t_(m) (t_(m)=τ) by dividing time into m timepieces, B_(j) ^(z)(t_(k))={Σ_(i)J_(ij)s_(i) ^(z)(t_(k−1))+g_(j)+sgn(s_(j) ^(z)(t_(k−1)))·g_(pina)}·t_(k)/τ or B_(j) ^(z)={Σ_(i)J_(ij)s_(i) ^(z)(t_(k−1))+g_(j)+g_(pinb)·s_(j) ^(z)(t_(k−1))}·t_(k)/τ is obtained by using a value of a variable s_(i) ^(z)(t_(k−1)) (i=1, 2, . . . , N) of previous time t_(k−1) and a coefficient g_(pina) or g_(pinb) of a relaxation term each time when the variable s_(j) ^(z)(t_(k)) is obtained at each time t_(k), and a function f is determined so as to cause the range of the variable s_(j) ^(z)(t_(k)) to become −1≦s_(j) ^(z)(t_(k))≦1 and results in s_(j) ^(z)(t_(k))=f(B_(j) ^(z)(t_(k)), t_(k)), the variable s_(j) ^(z) is caused to approach −1 or 1 by making a time step advance from t=t₀ to t=t_(m), and finally determines a solution in such a way that if s_(j) ^(z)<0, then s_(j) ^(zd)=−1 and otherwise, if s_(j) ^(z)>0, then s_(j) ^(zd)=1.

The coefficient g_(pinb) is, for example, a value from 50% to 200% of an average value of |J_(ij)|. With respect to the local field g_(j) for setting of a problem to be solved, it is possible to add a correction term δg_(j′) to g_(j′) only for a certain site j′ and increase the size of g_(j′) only for the site j′. The correction term δg_(j′) is, for example, a value from 10% to 100% of the average value of |J_(ij)|.

Example 1

In Example 1, a principle of the present invention will be described through migration to a classical form starting from quantum mechanical description.

Ising spin-Hamiltonian ground state search problem given by Equation (3) includes a classification problem called NP-hard and is known to be a useful problem (NPL 3).

$\begin{matrix} {{\hat{H}}_{p} = {{- {\sum\limits_{i > j}{J_{ij}{\hat{\sigma}}_{i}^{z}{\hat{\sigma}}_{j}^{z}}}} - {\sum\limits_{j}{g_{j}{\hat{\sigma}}_{j}^{z}}}}} & \left\lbrack {{Equation}\mspace{14mu} 3} \right\rbrack \end{matrix}$

J_(ij) and g_(j) are problem to be solved-setting parameters and σ̂_(j) ^(z) takes eigenvalues of ±1 in the z-component of Pauli spin matrix. i and j represent sites of spin. Ising spin is a variable taking only ±1 as a value such that an Ising spin system is formed with the eigenvalues of ±1 of the σ̂_(j) ^(z) in Equation (3). Ising spin of Equation (3) needs not be literally spin and may be anything physically as long as the Hamiltonian is described by Equation (3). For example, it is possible to associate high and low of a logic circuit to ±1 and is also possible to associate a vertically polarized wave and horizontally polarized wave of light to ±1 or associate 0 phase and π phase to ±1. In the method of the present example, similar to adiabatic quantum computing, a computation system is prepared in the ground state of the Hamiltonian given by Equation (4) at time t=0.

$\begin{matrix} {{\hat{H}}_{0} = {{- \gamma}{\sum\limits_{j}{\hat{\sigma}}_{j}^{x}}}} & \left\lbrack {{Equation}\mspace{14mu} 4} \right\rbrack \end{matrix}$

γ is a proportional constant determined by the magnitude of an external field uniformly applied to all sites j and σ̂_(j) ^(x) is the x-component of Pauli spin matrix. If a computation system is spin itself, the external field means a magnetic field. Equation (4) is equivalent to the Hamiltonian obtained by applying a transverse magnetic field and a case where all spins are directed to the x-direction (γ>0) is the ground state. Although the Hamiltonian for setting of a problem is defined as the Ising spin system having only the z-component, the x-component of spin appears in Equation (4). Accordingly, spin in the computation process is a vector (Bloch vector) rather than Ising. Although the Hamiltonian of Equation (4) is started at t=0, the Hamiltonian gradually changes with the progress of time t and becomes the Hamiltonian described by Equation (3), and finally, the ground state of Equation (4) is obtained as a solution.

First, it considers how spin respond to the external field in a case of a 1-spin system. The Hamiltonian of the 1-spin system is given by Equation (5).

H=−B·{circumflex over (σ)}  [Equation 5]

Here, σ̂ represents three components of the Pauli spin matrix as a vector. In a case where spin is directed to a magnetic field direction, the ground state is written as <σ̂>=B/|B| by using <•> as a quantum mechanical expected value. In the adiabatic process, the system always tries to keep the ground state and thus, the spin direction always follows the direction of magnetic field.

The above discussion can be extended to a multi-spin system. At t=0, the Hamiltonian is given by Equation (4). This means that the magnetic field B_(j) ^(x)=γ is uniformly applied to all spins. At t>0, the x-component of the magnetic field is gradually weakened and becomes B_(j) ^(x)=γ(1−t/τ). Regarding the z-component, interspin interaction exists and thus, the effective magnetic field is represented as Equation (6).

$\begin{matrix} {{{\hat{B}}_{j}^{z}(t)} = {\frac{t}{\tau}\left( {{\sum\limits_{i \neq j}{J_{ij}{\hat{\sigma}}_{i}^{z}}} + g_{j}} \right)}} & \left\lbrack {{Equation}\mspace{14mu} 6} \right\rbrack \end{matrix}$

The spin direction is specified by a <σ̂_(j) ^(z)/<σ̂_(j) ^(x)> and thus, if spin direction follows the effective magnetic field, spin direction is determined by Equation (7).

{circumflex over (σ)}_(j) ^(z)

/

{circumflex over (σ)}_(j) ^(x)

=

{circumflex over (B)} _(j) ^(z)(t)

/

{circumflex over (B)} _(j) ^(x)(t)

  [Equation 7]

Although Equation (7) is quantum mechanical description, but since Equation (7) takes an expected value, Equation (7) is a related equation related a classical quantity unlike Equations (1) to (6). Since non-local correlation (quantum entanglement) of quantum mechanics does not exist in the classical system, the spin direction is completely determined by the local field of each site and Equation (7) determines the behavior of the classical spin system. Although non-local correlation exists in the quantum system and thus, Equation (7) is deformed, matters regarding this will be described in and after Example 2, and a classical system determined by Equation (7) will be described in the present example in order to describe a basic form of the invention.

FIG. 1 illustrates a timing chart (procedure 100) for obtaining the ground state of spin system. Description of FIG. 1 is related to a classical quantity and thus, spin of a site j is represented by s_(j) rather than σ̂_(j). Because of this, an effective magnetic field B_(j) of FIG. 1 is a classical quantity. At t=0, a right effective magnetic field B_(j) is applied to all sites and all spins s_(j) are initialized to the right direction. In accordance with the lapse of the time t, the magnetic field in the z-axis direction and interspin interaction are gradually applied, spin finally becomes the +z-direction or −z-direction and the z-component of spin s_(j) becomes s_(j) ^(z)=+1 or −1. Although time t is ideal to be continuous, time t is discrete in the actual computation process and convenience can be improved. In the following, a case of being discrete will be described.

Since the z-component as well as the x-component is applied, spin according to the present invention is vectorial spin. The behavior as a vector can be understood also from FIG. 1. Thus far, the y-component was not appeared, this is because an external field direction is taken in the xz-plane and thus, the y-component of the external field does not exist and accordingly, it becomes that <σ̂^(y)>=0. Although a three-dimensional vector having a magnitude of 1 (this is called the Bloch vector and a state can be described using a point on a sphere) is supposed as spin of the computation system, only two dimensions may be considered in a method for taking axes of the present example (a state can be described using a point on a circle). Since γ is constant, B_(j) ^(x)(t)>0 (γ>0) or B_(j) ^(x)(t)<0 (γ<0) is satisfied. In this case, the two-dimensional spin vector becomes possible to describe only a semicircle, and if s_(j) ^(z) is designated by [−1, 1], the two-dimensional spin vector is determined by one variable of s_(j) ^(z). Accordingly, although spin of the present invention is a two-dimensional vector, the spin may be denoted by one-dimensional continuous variables which use [−1, 1] as the range.

In a procedure 100 of FIG. 1, the effective magnetic field is obtained for each site at time t=t_(k) and spin direction at time t=t_(k) is obtained by Equation (8) using the value of the effective magnetic field.

s _(j) ^(z)(t _(k))/s _(j) ^(x)(t _(k))=B _(j) ^(z)(t _(k))/B _(j) ^(x)(t _(k))  [Equation 8]

Equation (8) is obtained by rewriting Equation (7) to the notation on the classical quantity and thus, the symbol of <•> is not added.

Next, an effective magnetic field at t=t_(k+1) will be obtained by using a value of spin at t=t_(k). If the effective magnetic field at each time is specifically written, the effective magnetic field becomes Equations (9) and (10).

$\begin{matrix} {{B_{j}^{x}\left( t_{k + 1} \right)} = {\left( {1 - \frac{t_{k + 1}}{\tau}} \right)\gamma}} & \left\lbrack {{Equation}\mspace{14mu} 9} \right\rbrack \\ {{B_{j}^{z}\left( t_{k + 1} \right)} = {\frac{t_{k + 1}}{\tau}\left( {{\sum\limits_{i \neq j}{J_{ij}{s_{i}^{z}\left( t_{k} \right)}}} + g_{j}} \right)}} & \left\lbrack {{Equation}\mspace{14mu} 10} \right\rbrack \end{matrix}$

In the following, spin and the effective magnetic field are alternately obtained in accordance with a procedure schematically illustrated in the procedure 100 of FIG. 1.

In a classical system, the magnitude of a spin vector is 1. In this case, each component of spin vector is described as s_(j) ^(z)(t_(k))=sin θ and s_(j) ^(x)(t_(k))=cos θ by using the parameter θ defined in tan θ=B_(j) ^(z)(t_(k))/B_(j) ^(x)(t_(k)). This is rewritten again like s_(j) ^(z)(t_(k))=sin(arctan(B_(j) ^(z)(t_(k))/B_(j) ^(x)(t_(k)))), s_(j) ^(x)(t_(k))=cos(arctan(B_(j) ^(z)(t_(k))/B_(j) ^(x)(t_(k)))).

As is evident from Equation (9), only t_(k) is the variable of B_(j) ^(x)(t_(k)) and τ and γ are constants. Accordingly, s_(j) ^(z)(t_(k))=sin(arctan(B_(j) ^(z)(t_(k))/B_(j) ^(x)(t_(k)))) and s_(j) ^(x)(t_(k))=cos(arctan(B_(j) ^(z)(t_(k))/B_(j) ^(x)(t_(k)))) may be generally represented to be s_(j) ^(z)(t_(k))=f₁(B_(j) ^(z)(t_(k)),t_(k)) and s_(j) ^(x)(t_(k))=f₂(B_(j) ^(z)(t_(k)),t_(k)) as a function having B_(j) ^(z)(t_(k)) and t_(k) as variables.

Although spin is described as the two-dimensional vector and thus two components of s_(j) ^(z)(t_(k)) and s_(j) ^(x)(t_(k)) appear, if B_(j) ^(z)(t_(k)) is determined based on Equation (10), s_(j) ^(x)(t_(k)) is not needed. This responds to the matters that a spin state can be described by only s_(j) ^(z)(t_(k)) having [−1, 1] as a range. The final solution s_(j) ^(zd) needs to become s_(j) ^(zd)=−1 or 1 and it is assumed that if s_(j) ^(z)(τ)>0, then s_(j) ^(zd)=1, and otherwise, if s_(j) ^(z)(τ)<0, then s_(j) ^(zd)=−1.

FIG. 2 illustrates a flowchart in which the algorithm described above is summarized. Here, t_(m)=τ. Each of steps 101 to 109 of the flowchart of FIG. 2 corresponds to processing at a certain time of the procedure 100 of FIG. 1 throughout a period of time from t=0 to t=τ. That is, each of steps 102, 104, and 106 of the flowchart corresponds to Equations (9) and (10) at each of t=t₁, t_(k+1), and t_(m). The final solution is determined in step 108 in such a way that if s_(j) ^(z)<0, then s_(j) ^(zd)=−1, and otherwise, if s_(j) ^(z)>0, then s_(j) ^(zd)=1 (109).

Thus far, it is indicated how a problem to be solved is solved in a case where the problem to be solved is represented by Equation (3). Next, description will be made by enumerating a specific example as to how a specific problem to be solved is represented by Equation (3) including a local field g_(j) and intervariable interaction J_(ij) (i, j=1, 2, . . . , N). For example, a problem of electrical power supply management is considered as the specific problem to be solved. In this case, the local field is an amount of a natural phenomenon such as a temperature, or an electrical power use amount. That is, it is assumed that the temperature of each district is represented by the local field g_(j) (j=1-10), the electrical power use amount of public facilities (library, theater, supermarket, and the like) in each district is represented by the local field g_(j) (j=11-20), and the electrical power use amount of each household is represented by the local field g_(j) (j=21-100).

σ̂_(j) ^(z) (j=11-100) is a variable indicating where electrical power is to be distributed. However, j=1-10 is a subscript representing the temperature and thus, σ̂_(j) ^(z) (j=1-10) does not represent electrical power distribution and considers the temperature as a variable which influences activities of public facilities or households. The temperature is determined by a natural phenomenon and is hardly influenced by artificial factors and thus, the local field g_(j) (j=1-10) is set to a large value so that σ̂_(j) ^(z) (j=1-10) is not influenced by other variables.

Correlation intensity between the temperature and activities of the public facilities and households is represented through intervariable interaction J_(ij). The correlation of the temperature and electrical power use is also influenced by the concept of electrical power sharing that is proposed in recent years. For example, the electrical power sharing is a movement that tries to reduce electrical power of each household in such a way that household members go to the public facilities without using an air conditioner at each household in a period of time at which air-conditioning is needed. The movement is represented by allowing a non-zero value of intervariable interaction J_(ij) to be taken with respect to the subscript i=11-20 which represents the public facilities and the subscript j=21-100 which represent the house. However, interaction based on this concept is smaller compared to direct correlation on the temperature and activities of households and thus, the value of intervariable interaction J_(ij) is relatively small. Respective households do not live independently and influence on each other and thus, intervariable interaction J_(ij) (i, j=21-100) also becomes finite. The intervariable interaction J_(ij) is specifically set through the discussion as described above and the optimum electrical power supply distribution (eigenvalue of σ̂_(j) ^(z)=+1 or −1) is obtained through the ground state search of Equation (3)

In a case where it is unable to represent σ̂_(j) ^(z) for each item by a single variable, a plurality of σ̂_(j) ^(z) may be used and according to this, the plurality of the local fields g_(j) and intervariable interaction J_(ij) may also be used for each item. Although σ̂_(j) ^(z) is the variable representing electrical power distribution, σ̂_(j) ^(z) is correlated with human movement and an opening situation of public facilities. For that reason, it can be interpreted as “certain public facility is closed” by the obtained solution.

Description as above is a simple example that represents a specific problem to be solved by Equation (3). The specific problem to be solved to which the present example is applicable is not limited to the problem of electrical power supply management as exemplified as above and is applicable to a lot of problems to be solved, for example, travel route optimization, vehicle guidance for avoiding congestion, circuit design, product supply management, scheduling, and financial assets selection.

Example 2

In Example 1, the transition to the classical quantity was made by taking the expected value based on a quantum mechanical equation, and the algorithm by the classical quantity was described using FIG. 1 and FIG. 2. A major aim of Example 1 is to describe the algorithm of the present invention and thus, description was made without including the quantum mechanical effect. However, if the quantum mechanical effect is added, improvement of a correct answer rate or improvement of a computation speed can be expected. In Example 2, description will be made on a method in which algorithm itself is a classical but a correction parameter is added based on quantum mechanics in order to improve performance.

There are a linear superposition state and quantum entanglement (non-local correlation) as the characteristics of quantum mechanics. For example, a quantum bit that takes two states of |0> and |1> is considered. The linear superposition state is one that is the sum of two states as |ψ>=α|0>+β|1>. The nature of the linear superposition state is already incorporated by vectorially treating spin in Example 1. That is, if s_(j) ^(z)(t_(k))=1, then the state is |0>, and otherwise, if s_(j) ^(z)(t_(k))=−1, then the state is |1>. |0> and |1> correspond to the state in a case where the z-axis is selected as an quantization axis of spin, and a case of s_(j) ^(x)(t₀)=1 which is directed to the x-axis is represented |ψ(t₀)>=(|0>+|1>)/√2. If s_(j) ^(x)(t)=−1, then the state is |ψ(t₀)>=(|0>−|1>)/√2. Taking the x-axis into consideration means that linear superposition is considered.

In the present example, the quantum entanglement which is the quantum mechanical effect will be described. As an example, a case where a state of a 2-quantum bit system can be written as |ψ>=α|00>+β|11> is considered. It is expressed that |α|²+|β|²=1 by the standardization condition. The first variable and the second variable of |00> and |11> represent a first quantum bit and a second quantum bit, respectively. Since σ̂_(j) ^(z)|0>=|0> and σ̂_(j) ^(z)|1>=−11> as nature of the Pauli spin matrix, it becomes that σ̂₁ ^(z)|ψ>=α|00>−|11> and <ψ|σ̂₁ ^(z)|ψ>=|α|²−|β|². Since σ̂₁ ^(x)|0>=∥> and σ̂₁ ^(x)|1>=|0>, it becomes that σ̂₁ ^(x)<|ψ>=α|10>+β|01> and <ψ|σ̂₁ ^(x)|ψ>=0. Since σ̂₁ ^(y)|0>=i|1> and σ̂₁ ^(y)|1>=−i|0>, it becomes that σ̂₁ ^(y)|ψ>=iα|10>−iβ|01> and <ψ|σ̂₁ ^(y)|ψ>=0. Accordingly, it is expressed that <σ̂₁ ^(x)(τ)>²+<σ̂₁ ^(y)(τ)²+σ̂₁ ^(z)(τ)>²=(|α|²−|β|²)². As an extreme example, in a case of α=β at which quantum entanglement is maximized, it becomes that <σ̂₁ ^(x)(τ)>²+<σ̂₁ ^(y)(τ)>²+<σ̂₁ ^(z)(τ)>²=0 and the magnitude of the first spin vector becomes 0. Such a situation does not occur in the absence of quantum entanglement. For example, 1-spin system is considered and if a state is assumed to be |ψ>=α|0>+β|1>, then it becomes that <ψ|σ̂₁ ^(z)|ψ>=|α|²−|β|², <ψ|σ̂₁ ^(x)|ψ>=α*β+αβ*, <ψ|σ̂₁ ^(y)|ψ>=iαβ*−iα*β, and <σ̂₁ ^(x)(τ)>²+<σ̂₁ ^(y)(τ)>²+<σ̂₁ ^(z)(τ)>²=(|α|²+|β|²)²=1, and accordingly, the magnitude is certainly saved to 1.

As described above, although it is one example, it was found out that in a case where quantum entanglement is present, the magnitude of spin vector is not saved to 1. In a classical system, the magnitude of spin vector is a fixed value of 1, but if quantum entanglement is present, the magnitude of spin vector is not 1. In Example 1, on the premise that the magnitude of spin vector is 1, it was set in such a way s_(j) ^(z)(t_(k))=sin θ and s_(j) ^(x)(t_(k))=cos θ by using θ defined by tan θ=<B_(j) ^(z)(t)>/<B_(j) ^(x)(t)> as a parameter. However, in this method, nature of quantum entanglement inherent in the system is not reflected. Here, the way to reflect nature of quantum entanglement is considered.

As described above, spin vector is not saved to 1. Here, a correction parameter r_(s) (0<=r_(s)<=1) representing the magnitude of spin is defined (“<=” means “greater than or equal to”). A proportional relationship of Equation (8) is not satisfied by being associated with matters that spin vector is not saved to 1. Here, the correction parameter r_(B) is defined to deform Equation (8) to Equation (11).

s _(j) ^(z)(t _(k))/s _(j) ^(x)(t _(k))=r _(B) B _(j) ^(z)(t _(k))/B _(j) ^(x)(t _(k))  [Equation 11]

Similar to the case of Example 1, an angle θ representing the spin direction is defined by tan θ=s_(j) ^(z)(t_(k))/s_(j) ^(x)(t_(k)). If Equation (11) is applied to this, then tan θ=r_(B)B_(j) ^(z)(t_(k))/B_(j) ^(x)(t_(k)). Considering that the magnitude of spin is r_(s), it becomes that s_(j) ^(z)(t_(k))=r_(s)·sin θ and s_(j) ^(x)(t_(k))=r_(s)·cos θ. By these related equations, effects of quantum entanglement are incorporated into the classical algorithm through the correction parameters r_(s) and r_(B). If it is notated by not using θ, it becomes that s_(j) ^(z)(t_(k))=r_(s)·sin(arctan(r_(B)·B_(j) ^(z)(t_(k))/B_(j) ^(x)(t_(k)))) and s_(j) ^(x)(t_(k))=r_(s)·cos(arctan(r_(B)·B_(j) ^(z)(t_(k))/B_(j) ^(x)(t_(k)))). If r_(s) and r_(B) are incorporated into functions f₁ and f₂, then s_(j) ^(z)(t_(k))=f₁(B_(j) ^(z)(t_(k)) t_(k)) and s_(j) ^(x)(t_(k))=f₂(B_(j) ^(z)(t_(k)),t_(k)).

It is preferable that the correction parameters r_(s) and r_(B) are originated in quantum entanglement and finely controlled depending on t_(k), s_(j) ^(z)(t_(k)) and s_(j) ^(x)(t_(k)). However, it is difficult to accurately acquire information about quantum entanglement in principle and it is necessary to consider any coping method. Although it is actually determined semi-empirically depending on a problem, a coarse determining method becomes as follows. The r_(B) is an amount of which the sign can be changed and is an amount obtained by most properly reflecting quantum entanglement. On the other hand, the r_(s) is a correction factor satisfying 0<=r_(s)<=1 and has a role smaller than the r_(B). Accordingly, the r_(s) may be set to be r_(s)=˜1 over the total computation time (“=˜” means “approximately equal to”) and mainly incorporates quantum effects by the r_(B). Since there is no quantum entanglement at the start of the computation, r_(B)=1 at t=0 and r_(B) gradually comes closer to 0 at t>0. If it comes closer to t=τ, many of spins converge to s_(j) ^(z)=1 or −1, but some of spins behave subtly of whether to become s_(j) ^(z)>0 or s_(j) ^(z)<0. What ultimately determines success or failure of the computation is these spins having bad convergence. Accordingly, at t=˜τ, the r_(B) is determined so as to be most suitable for these spins. Effects of quantum entanglement are incorporated as much as possible and thus, r_(B)=˜0. The direction of spin converging to s_(j) ^(z)=1 or −1 is stable such that there is little adverse effect according to the fact that r_(B)=˜0.

Description as above is the method for setting the r_(B) regarding time dependency. It is also effective to provide magnetic field dependency to r_(B). In a case of B_(j) ^(z)(t_(k))/B_(j) ^(x)(t_(k))=˜0, s_(j) ^(z)(t_(k))/s_(j) ^(x)(t_(k)) becomes inevitably indefinite. Accordingly, in a case of B_(j) ^(z)(t_(k))/B_(j) ^(x)(t_(k))=˜0, it is effective to speed up a change in which r_(B) becomes from r_(B)=˜1 to r_(B)=˜0 with the progress of time t, in comparison with a case of |B_(j) ^(z)(t_(k))/B_(j) ^(x)(t_(k))|>>0.

Although in a case where there is no special intersite features, the r_(s) or r_(B) is not allowed to have site dependency, in a case where the feature per site is known in advance, it may be needed to respond to each feature and if the r_(s) and r_(B) become site dependent, improvement of the correct answer rate of a solution can be expected.

FIG. 3 illustrates a flowchart in a case where the r_(s) and r_(B) are introduced. The difference between the flowcharts of FIG. 2 and FIG. 3 is that steps 103, 105, and 107 are changed to steps 103 a, 105 a, and 107 a, that include the correction parameters r_(s) and r_(B), respectively. In this example, as described above, the correction parameters r_(s) and r_(B) are added to the function f, θ is defined by tan θ=r_(B)·B_(j) ^(z)(t_(k))/B_(j) ^(x)(t_(k)), s_(j) ^(z)(t_(k)) is determined by s_(j) ^(z)(t_(k))=r_(s)·sin θ, and accordingly, the function f becomes f(B_(j) ^(z)(t_(k)) t_(k))=r_(s)·sin(arctan(r_(B)·B_(j) ^(z)(t_(k))/B_(j) ^(x)(t_(k)))). It is desirable that the correction parameters r_(s) and r_(B) are finely controlled depending on t_(k) and B_(j) ^(z)(t_(k)).

Example 3

In Examples 1 and 2, the Hamiltonian of the problem to be solved was given by Equation (3) and the effective magnetic field of each site was given by Equations (9) and (10). The local field g_(j) was present in each site. If the local field g_(j) is present, a direction of s_(j) ^(z) determined by g_(j) becomes a zero-approximation direction and the direction of s_(j) ^(z) is corrected with application of interaction determined by J_(ij). However, in a case where all sites becomes g_(j)=0, a concept of the zero-approximation direction is not present and a degeneration number is increased and thus, whether it becomes s_(j) ^(z)>0 or s_(j) ^(z)<0 is not determined and, s_(j) ^(z) is not escaped from s_(j) ^(z)=˜0 even if the computation time has elapsed. When it is not the correct answer even if s_(j) ^(z) is escaped therefrom, a force, which reverses the direction between spins, is applied to each other as a result from interspin interaction, a vibration phenomenon in which the direction of spin is reversed occurs at each step of time, and the solution is not converged.

In order to solve these problems, a relaxation term (pin fixture term) is added to Equation (10) to allow the effective magnetic field to be set as Equation (12A).

$\begin{matrix} {{B_{j}^{z}\left( t_{k + 1} \right)} = {\frac{t_{k + 1}}{\tau}\left( {{\sum\limits_{i \neq j}{J_{ij}{s_{i}^{z}\left( t_{k} \right)}}} + g_{j} + {{{sgn}\left( {s_{j}^{z}\left( t_{k} \right)} \right)} \cdot g_{pina}}} \right)}} & \left\lbrack {{Equation}\mspace{14mu} 12A} \right\rbrack \end{matrix}$

The third term is the relaxation term. sgn(•) is a sign function and sgn(s_(j) ^(z))=1 for s_(j) ^(z)>0, sgn(s_(j) ^(z))=0 for s_(j) ^(z)=0, and sgn(s_(j) ^(z))=−1 for s_(j) ^(z)<0. The relaxation term serves to keep based on spin direction to eliminate the above-described vibration phenomenon and improve convergence of a solution. The value of g_(pina) is empirically determined. The relaxation term is an additional term for improving convergence of a solution and needs to be sufficiently smaller than |J_(ij)|. On the other hand, if it is too small, enough work cannot be expected. If a range is specified, it is considered appropriate that the coefficient g_(pina) is adjusted to be a value from 1% to 50% of the average value of |J_(ij)|. As a guide, the coefficient g_(pina) may be set to about 1/10 of the average value of |J_(ij)|.

The relaxation term (third term) of Equation (12A) depends only on the sign of s_(j) ^(z) and does not depend on the size of s_(j) ^(z). On the other hand, the first term depends on the size of s_(j) ^(z). There is a method in which the third term also depends on the size of s_(j) ^(z). Such a case corresponds to Equation (12B).

$\begin{matrix} {{B_{j}^{z}\left( t_{k + 1} \right)} = {\frac{t_{k + 1}}{\tau}\left( {{\sum\limits_{i \neq j}{J_{ij}{s_{i}^{z}\left( t_{k} \right)}}} + g_{j} + {g_{pinb} \cdot {s_{j}^{z}\left( t_{k} \right)}}} \right)}} & \left\lbrack {{Equation}\mspace{14mu} 12B} \right\rbrack \end{matrix}$

The size of the third term in a case of Equation (12B) is changed depending on the size of s_(j) ^(z) and thus, the coefficient g_(pinb) typically has a size of about an average value of |J_(ij)| and is in the order of 50% to 200% in a range point of view.

In a case where the ground state is degenerated, it is necessary to induce computation into one solution. When the computation is not induced, it is not escaped from s_(j) ^(z)=˜0 which is the average value of the solution. The relaxation term is also useful for this induction. It is necessary to appropriately set a zero-approximation solution in order to induce the computation to one solution. Here, it is assumed that a single site (assumed as j site) assumed to be a reference is selected and set as s_(j) ^(z)(t₀)=1 and the direction of other sites is determined based on the sign of J_(ij) by using the j site as a reference at an early stage. In this way, appropriate setting of zero-approximation is made and the computation is converged to one correct answer through the computation of the local field response after the setting. In this case, the relaxation term contributes as in the following.

At t=t₀, it is set as s_(j) ^(z)(t₀)=1 and s_(j) ^(z)(t₀)=˜0 (i≠j), Equation (12A) or Equation (12B) is time-evolved based on Equation (11). Here, s_(i) ^(z)(t₀)=˜0 (i≠j) means that it is set to, for example, about s_(i) ^(z)(t₀)= 1/1000, so that the s_(i) ^(z) hardly influences on other sites. By the time step t₀→t₁, s_(i) ^(z)>0 or s_(i) ^(z)<0 is obtained for an i site at which J_(ij) becomes J_(ij)≠0 based on Equation (12A) or Equation (12B) and Equation (11). Although initial setting of s_(j) ^(z)(t₀)=1 is not propagated at step of t₀→t₁ for the i site at which J_(ij) becomes J_(ij)=0, the number of sites at which s_(i) ^(z) becomes s_(i) ^(z)>0 or s_(i) ^(z)<0 is increased and thus, the initial setting of s_(j) ^(z)(t₀)=1 is indirectly propagated through the site in the next time step and almost all sites become s_(i) ^(z)>0 or s_(i) ^(z)<0 at an early stage of the computation. Since the relaxation term is present, s_(j) ^(z)>0 of the j site is maintained at an early stage of the computation and information of s_(j) ^(z)(t₀)=1 is propagated to almost all sites while a history of s_(j) ^(z)(t₀)=1 remains in the j site itself. This is the appropriate setting of zero-approximation.

FIG. 4 illustrates a flowchart in a case where the relaxation term is added. FIG. 4A corresponds to Equation (12A) and FIG. 4B corresponds to Equation (12B). The difference between the flowcharts of FIG. 3 and FIG. 4 is that steps 102, 104, and 106 are changed to steps 102 b, 104 b, and 106 b that include the relaxation term.

As described above, description was made on matters that setting of s_(j) ^(z)(t₀)=1 and s_(i) ^(z)(t₀)=˜0 (i≠j) are made at t=t₀ as a method for inducing the computation to one solution in a case where the ground state is degenerated. In this method, a mandatory factor is only s_(j) ^(z)(t₀)=1 at t=t₀. At t>t₀, only the history by the relaxation term is present and the mandatory factor is not present. It is effective to add the mandatory factor over all the time in order to further increase inductiveness to one solution. To do so, a method in which a local field term δg_(j′) is additionally added only to one site j′ is considered. That is, δg_(j′) is added to the local field term g_(j′) of Equations (12A) and (12B), which results in g_(j′)→g_(j′)+δg_(j′). Here, in two amounts of the right-hand side, the δg_(j′) is assigned the same sign as the sign of g_(j′) so as not to cancel each other. If it is originally g_(j′)=0, the sign of δg_(j′) may be either positive or negative. The spin of j′ site is strongly induced in a certain direction by the additional term. Although it is proper that δg_(j′) is adjusted to be in a range of about from 10% to 100% of the average value of |J_(ij)|, one guide may be set to a range of about 50% of the average value of |J_(ij)|. Furthermore, since the mandatory factor is added through δg_(j′), in this case, initial setting of s_(j′) ^(z)(t₀)=1 is not needed and s_(i) ^(z)(t₀)=˜0 may be set for all sites. A flowchart for this case is illustrated in FIG. 4C and FIG. 4D. FIG. 4C corresponds to Equation (12A) and FIG. 4D corresponds to Equation (12B).

As described above, the relaxation term is added to thereby suppress vibration related to the direction of spin in time evolution and improve convergence of the solution. Furthermore, the relaxation term also has the following effects. In a case where the degeneration number of the ground state is large, there is a possibility that it is unable to determine whether which direction of the system orients is preferable in the ground state, the system falls into a state of s_(j) ^(z)=0 which is the average value of a solution, and the system falls into a situation without escaping from the state. Here, only spin of one site (assumed as site j) is clearly determined (s_(j) ^(z)=1 or −1) at an initial state and other spins are set as s_(k) ^(z)=˜0 (k≠j, s_(k) ^(x)=˜1), and a spin arrangement is determined by using the j site as a reference through spin interaction at an early stage of time evolution. Since the relaxation term is present, the direction of the j site is fixed and the reference is maintained at an early stage of the computation. For that reason, a good approximate solution corresponding to one of the degenerated solutions is implemented at an early stage of the computation and is induced to one of the degenerated solutions as it is. As such, the relaxation term increases the convergence of the solution and is converged to one of solutions in a case where the degeneration number is large. Furthermore, in a case where the degeneration number is large, there is also a second method as a method for causing the relaxation term to be converged to one of solutions. In Equations (12A) and (12B), the local field term δg_(j′) is additionally added to the site j′ of one solution to strongly induce only spin of j′ site in a certain direction. With this, it is strongly induced to one of the degenerated solutions relative to the j′ site set as a reference.

Example 4

In Example 3, description was made on matters that the convergence of the solution is improved by introducing the relaxation term. The relaxation term mainly exerts power on a case where all sites become g_(j)=0. If there is a term for which g_(j) becomes g_(j)≠0, convergence is relatively good even without adding the relaxation term. When determination is made as to whether the relaxation term is to be added based on whether it is g_(j)=0 for all sites, it becomes an efficient computing method using only necessary terms.

FIG. 5 illustrates a flowchart of an example of algorithm for this case. FIG. 5A to FIG. 5D correspond to FIG. 4A to FIG. 4D, respectively.

Example 5

As understood from Equation (1) or the like described above, although the computation time is assumed as τ, there are several methods as a final solution determination method. Various solution determination methods will be described by using Example 5.

FIG. 6 is a diagram illustrating the flowchart of an example of algorithm related to the final solution determination method.

In the first method, if s_(j) ^(z)>0, then s_(j) ^(zd)=1 and if s_(j) ^(z)<0, then s_(j) ^(zd)=−1 at t=τ(t=t_(m)) (115), as illustrated in FIG. 6A. The flowchart of each example of FIG. 2 to FIG. 5 describes this case and the flowchart focused only on a solution determination method corresponds to FIG. 6A.

In the second method, as illustrated in FIG. 6B, convergence of s_(j) ^(z) is seen and thus, if the sign of s_(j) ^(z) is not changed for all time t_(q) during a sufficient period of time Δt after a certain time t_(k) (121), it is determined, based on the sign s_(j) ^(z) at that time, that s_(j) ^(zd)=1 or −1 (122).

In the third method, as illustrated in FIG. 6C, although the computation continues until t=τ (t=t_(m)), similar to the first method (115), energy at each stage is obtained based on Equation (3). Since eigenvalues of σ̂_(j) ^(z) of Equation (3) are ±1, it is determined whether eigenvalue of ̂_(j) ^(z) is 1 or −1, according to the sign of s_(j) ^(z) at each stage in the computation process. That is, if s_(j) ^(z)(t_(k))>0, then eigenvalue of ̂_(j) ^(z) is 1 (s_(j) ^(zd)=1) and if s_(j) ^(z)(t_(k))<0, then eigenvalue of ̂_(j) ^(z) is −1 (s_(j) ^(z)=−1). Calculating using eigenvalue of ̂_(j) ^(z) is in regards to energy and s_(j) ^(z)(t_(k)) (−1<=s_(j) ^(z)(t_(k))<=1) is used in the computation process. Δt the time when it reaches t=τ (t=t_(m)), energy at respective times t_(k) is compared and the final solution is determined based on the sign of s_(j) ^(z)(t_(k′)) at time t_(k′) at which the lowest energy is obtained.

That is, by assuming that if s_(j) ^(z)(t_(k))<0 at each time t_(k), then s_(j) ^(zd)(t_(k))=−1, and otherwise, if s_(j) ^(z)(t_(k))>0, then s_(j) ^(zd)(t_(k))=1 (119), H_(p)(t_(k))=−Σ_(i>j)J_(ij)s_(i) ^(zd)(t_(k))s_(j) ^(zd)(t_(k))−Σ_(j)g_(j)s_(j) ^(zd)(t_(k)) is calculated at each time t_(k) (123), and it is assumed that s_(j) ^(zd)(t_(k′)) at time t_(k′), at which H_(p)(t_(k)) became the minimum value, is the final solution (124).

In the fourth method, as illustrated in FIG. 6D, the computation is stopped at a stage where all s_(j) ^(z) are converged, similar to the second method. However, the final solution is not determined from s_(j) ^(z) at a time-point where the computation is stopped, energy at each time is calculated similar to the third method, and the final solution is determined from s_(j) ^(z)(t_(k′)) at time when the lowest energy was given (125). Whether to take any of the methods will be decided by the user.

Example 6

The example in which the time axis is discretely set as illustrated in FIG. 1 was described. Although a continuous change is ideal and thus fine time intervals are appropriate, if the time intervals are taken too finely, the computation time becomes longer. Changing time intervals in accordance with the progress of the computation is considered.

Important time in the computation process is time when the sign of s_(j) ^(z) changes. The change in the sign of s_(j) ^(z) is relatively small near the start or end of the computation and the change in the sign of s_(j) ^(z) is violent in an intermediate stage of the computation. As the first method, there is a setting method in which the time interval is programmatically set to be larger at the start of computation, the time interval is decreased over time, and then the time interval is increased.

In the second method, a possibility of spin reversal is evaluated at each time and the time interval is set based on the evaluation. For example, it becomes as follows. If the sizes of |s_(j) ^(z)| are roughly equal at all spins, the possibility of occurrence of spin reversal is low. In this case, the time interval is increased. On the other hand, if a size of |s_(j) ^(z)| of a specific spin is smaller than other spins, a probability that spin reversal occurs is high. In this case, the time interval is decreased. One specific example of a time interval determination is as follows. The minimum time interval is set as δt_(min). A mean square of spins of all sites at time t_(k) is set as s_(ave)(t_(k))², and the size of square of the minimum spin is set as s_(min)(t_(k))². That is, s_(ave)(t_(k))²=Σ_(j)(s_(j) ^(z)(t_(k)))²/N, s_(min)(t_(k))²=min s_(j) ^(z)(t_(k))². [x] is set to ΔT_(k+1,k)=t_(k+1)−t_(k)=δt_(min)×max (1, [100×(s_(min)(t_(k))²/s_(ave)(t_(k))²)^(1/2)]) as the maximum integer which is less than or equal to x. In a case of this example, the minimum value of the time interval becomes σt_(min) and the maximum value of the time interval becomes 100·δt_(min).

Whether to take any of the methods will be decided by the user.

Example 7

In Example 3, the site j was selected and s_(j) ^(z)(t₀) was set as s_(j) ^(z)(t₀)=1 and s_(i) ^(z)(t₀)=˜0 (i≠j). Since the site j is optional, it is possible to solve the same problem by changing selection of the site j. In this way, if an optimum solution, which is attained by repeatedly solving the same problem, is selected, the correct answer rate is improved.

Example 8

In Examples 1 to 7, description was made on a computation principle and computation algorithm. In Example 8, first, description will be made on a configuration example of a computer which causes algorithm to be operated as a program.

FIG. 7 illustrates an example of a computer configuration of the present example. The configuration illustrated in FIG. 7 is basically the same as a normal computer, and data is transferred from a main storage device 201 which is a storing unit to a computing device 202 which is a computing unit and after the computation, data is returned to the main storage device 201. The computation proceeds by repeating the transfer and return of data. The control tower for this case is a control device 203 as a control unit. The computation in the present example is executed by the computing device 202 by time and per a site according to a flow of FIG. 2 to FIG. 5.

The program executed by the computing device 202 is stored in the main storage device 201 which is the storing unit. In a case where a storage capacity of the main storage device 201 is not enough, an auxiliary storage device 204 which is the same storing unit is used. An input device 205 is used for inputting data, the program, and the like and an output device 206 is used for outputting a result. The input device 205 includes an interface for network connection in addition to a manual input device such as a keyboard. The interface also serves as the output device. Although the algorithm described in Examples 1 to 7 as the program is applied to the configuration of FIG. 7, a normal computer is used as the apparatus itself.

On the other hand, there is also a method in which the computation principle and algorithm described in Examples 1 to 7 are used, including execution of the program as well as an apparatus configuration.

FIG. 8 illustrates a configuration diagram in which those matters described above are realized. A local field response computing device 1000 is included as the computing unit. This is a difference in comparison with the configuration of FIG. 7. The local field response computing device 1000 is a dedicated computing unit for conducting the algorithm described above and executes only the local field response computation described in Examples 1 to 7, and a general process is conducted by the computing device 202. Information used by the local field response computing device 1000 is transferred from the main storage device 201.

As information that is needed, there are a time parameter t_(k), correction parameters r_(s) and r_(B) related to quantum entanglement, coefficients g_(pina) and g_(pinb) of the relaxation term, and the like, in addition to the problem to be set parameters called intervariable interaction J_(ij) and the local field g_(j). Processing which takes, for example, synchronization is a role of the control device 203 similar to the computer of the configuration of FIG. 7. Information of progress as well as the final result is transferred from the local field response computing device 1000 to the main storage device 201 at each time parameter t_(k), as needed, according to an instruction of the control device 203. In an intermediate stage of computation, a transferred value from the local field response computing device 1000 is used in, for example, the calculation of energy of the Ising spin and Hamiltonian at an intermediate stage of computation or evaluation of s_(min)(t_(k))² and s_(ave)(t_(k))².

In the third and fourth method described in Example 5, as illustrated in FIG. 6C and FIG. 6D, the final solution is determined by using the value of energy of the Ising spin and Hamiltonian at an intermediate stage of computation. If it is determined whether s_(j) ^(zd) is +1 or −1, the calculation of energy of the Ising spin and Hamiltonian is simple one and a normal computing device 202 is used. The local field response computing device 1000 is a dedicated computing unit for the local field response computation described in Examples 1 to and the normal computing device 202 is used for other processing.

Example 9

The local field response computing device 1000 described in Example 8 can be realized by various methods. In the present example, a method for efficiently using parallelism of light will be described first.

FIG. 9 illustrates an entire image. Information such as γ, g_(j), and g_(pina) needed for computation is sent from the main storage device 201 to the control unit 1100 and information of J_(ij) is sent from the main storage device 201 to a variable mask 1120. g_(pinb) in a case of being corresponded to Equation (12B) is sent to the variable mask 1120 and J_(ii) is set as J_(ii)=g_(pinb). Output intensity of an LED array 1110 is assumed to reflect a value of the variable s_(j) ^(z). In the present example, only intensity information of light is used and phase information is not used. For that reason, an incoherent light source such as an LED array is used as a light source 1110. Although it is possible to use an LD which is coherent, in such a case, measurement time in a detector array 1130 is taken to be sufficiently long so as not to make interference effect between output light beams of an LD array appear. Output light beams from the LED array 1110 extend only the lateral direction, attenuate light quantity according to J_(ij) in the variable mask 1120, and then make the longitudinal direction converge to be converted into an electrical signal in a detector array 1130.

FIG. 10 is a configuration diagram illustrating a configuration example of an optical part of FIG. 9. An optical system spanning from the LED array 1110 to the detector array 1130 uses, for example, a lens system of FIG. 10.

Although the variable s_(j) ^(z) takes a value of [−1,1], an output of the light source is not able to take a negative value. Accordingly, it is assumed that s_(j) ^(z) is represented in a pair of two LEDs in the LED array 1110. That is, it is assumed that for s_(j) ^(z)>0, s_(j) ^(z) is set as s_(j) ^(z+)=s_(j) ^(z) and s_(j) ^(z−)=0 and for s_(j) ^(z)<0, s_(j) ^(z) is set as s_(j) ^(z+)=0, and s_(j) ^(z+)=s_(j) ^(z)=−|s_(j) ^(z)|, one of the LED output is set as s_(j) ^(z+), the other is set as |s_(j) ^(z−)|, and thus, s_(j) ^(z) is represented by a difference of both outputs s_(j) ^(z)=s_(j) ^(z+)−|s_(j) ^(z−)|=s_(j) ^(z+)+s_(j) ^(z−). The detector array 1130 is also assumed to represent in a pair of two detectors in association with the light source side. With this, it is possible to correspond to the variable mask 1120 which is unable to take a negative value. A signal intended to be obtained in the detector array 1130 is b_(j) ^(z)≡Σ_(i)J_(ij)s_(i) ^(z). Similar to s_(j) ^(z), if J_(ij)=J_(ij) ⁺+J_(ij) ⁻=J_(ij) ⁺−|J_(ij) ⁻|, it becomes that b_(j) ^(z)=Σ_(i)J_(ij)s_(i) ^(z)=Σ_(i)(J_(ij) ⁺+J_(ij) ⁻)(s_(i) ^(z+)+s_(i) ^(z−))=Σ_(i)(J_(ij) ⁺s_(i) ^(z+)+J_(ij) ⁻s_(i) ^(z−))+Σ_(i)(J_(ij) ⁺s_(i) ^(z−)+J_(ij) ⁻s_(i) ^(z+)). Each of pairs of two detectors of the detector array 1130 detects b_(j) ^(z+)=Σ_(i)(J_(ij) ⁺s_(j) ^(z+)+J_(ij) ⁻s_(j) ^(z−)) and |b_(j) ^(z−)|=Σ_(i)(J_(ij) ⁺|s_(j) ^(z−)|+|J_(1j) ⁻|+|J_(ij) ⁻|s_(j) ^(z+)) and b_(j) ^(z)=b_(j) ^(z+)−|b_(j) ^(z−)|=b_(j) ^(z+)+b_(j) ^(z−) obtained by taking a difference between the detectors becomes the signal. Furthermore, as described above, if J_(ii) is set as J_(ii)=g_(pinb) it corresponds to Equation (12B).

If b_(j) ^(z) is obtained, B_(j) ^(z) based on Equation (10) or Equations (12A) and (12B) is obtained by adding g_(j) and g_(pina) terms. This calculation is performed by the control unit 1100. A calculation to obtain s_(j) ^(z) from B_(j) ^(z) is performed by the control unit 1100 and the value of s_(j) ^(z) is sent to the LED array 1110. Like this, 1 step spanning from time t_(k) to t_(k+1) is ended. Furthermore, the control unit 1100 is intended to repeat same processing and is a dedicated circuit for that. s_(j) ^(z) at each time is transferred to the main storage device 201 to be used for analysis.

Example 10

In the example of FIG. 9, an optical system spanning from the LED array 1110 to the detector array 1130 was free space. It can also be realized by an optical system in which a waveguide is used for that portion.

FIG. 11 illustrates that case. Light beams output from the LED array (LD array) 1110 are divided by a demultiplexer 1115, are transmitted through the variable attenuator 1120 (variable mask in FIG. 9) in which transmittance is set based on J_(ij), and then, are condensed by a multiplexer 1125 to be received by the detector array 1130. In FIG. 9, only a spatial optical system is changed to a waveguide optical system and an operation principle is the same. Accordingly, the LED array (LD array) 1110 represents the s_(j) ^(z) in a pair of two LEDs and the detector array 1130 also operates in a pair of two detectors.

Example 11

In Example 9 and Example 10, s_(j) ^(z) was represented in a pair of two light sources. If s_(j) ^(z+) and s_(j) ^(z−) are represented by using polarization, a single light source can be used for s_(j) ^(z).

FIG. 12 illustrates that case. Each of the LDs within a light source 1111 is for representing s_(j) ^(z). Distribution intensity to two polarized waves is adjusted by a polarized wave modulator 1112. If s_(j) ^(z+) is set as s_(j) ^(z+)=A sin(π/4+θ) and |s_(j) ^(z−)|=A cos(π/4+θ), s_(j) ^(z)=s_(j) ^(z+)+s_(j) ^(z−)=s_(j) ^(z+)−|s_(j) ^(z−)|=√2A sin θ and if θ is set as θ<<1, s_(j) ^(z)=˜√2Aθ. If a range of θ is set as −θ_(max)<=θ<=θ_(max), −1<=s_(j) ^(z)<=1 is satisfied by A=1/(√2θ_(max)) and selection. The polarized wave modulator 1112 modulates θ in s_(j) ^(z+)=A sin(π/4+θ) and |s_(j) ^(z−)|=A cos(π/4+θ). Light beams which are polarization modulated are polarization separated by a polarization separator 1113 and are guided to the demultiplexer 1115. A portion spanning from the demultiplexer 1115 to the detector 1130 operates similarly to that of FIG. 11. Similar to FIG. 11, the detector 1130 operates in a pair of two detectors and thus a difference signal is taken by the differential amplifier 1131 and difference signal is set as B_(j) ^(z)=Σ_(i)J_(ij)s_(i) ^(z). Thereafter, signal processing similar to the case of Example 9 where the signal is sent to the control unit 1100 is performed, a signal advanced by 1 step is sent to the polarized wave modulator 1112 and progression to the next step is performed.

In the present example, s_(j) ^(z)=√2A sin θ was set as s_(j) ^(z)=˜√2Aθ by setting θ as θ<<1. Here, if θ is set as θ=arcsin φ, s_(j) ^(z) becomes that s_(j) ^(z)=√2Aφ without imposing a condition of θ<<1. That is, an input signal to the polarized wave modulator 1112 is adjusted to thereby make it possible to maintain linearity. In Example 2, description was made on matters that r_(B) is shifted from r_(B)=1 to r_(B)=˜0 according to time variation from t=0 to t=τ. In this case, a functional form of θ=arcsin φ is further deformed.

Example 12

The local field response computing device 1000 may also be realized by an electrical circuit as well as the method using light as in Examples 9 to 11.

FIG. 13 illustrates a configuration example for that case. Information of each spin s_(i) ^(z) waits temporarily in a buffer array 1210. Information of J_(ij) is held in a memory 1221. g_(pinb) in a case of being corresponded to Equation (12B) is held in the memory 1221 and J_(ii) is set as J_(ii)=g_(pinb). In the computing component 1220, B_(j) ^(z)=Σ_(i)J_(ij)s_(i) ^(z) is sequentially calculated based on information of s_(i) ^(z) that waits in the buffer array 1210 and information of J_(ij) of the memory 1221 and is transferred to a buffer array 1230 and is saved. Thereafter, a signal is transferred to the control unit 1200, signal processing similar to the control unit 1100 in Example 9 is performed, and information of s_(i) ^(z) advanced by 1 step is sent to the buffer array 1210. This is processing of 1 step and this processing is repeated from t=t₀ to t=τ.

s_(i) ^(z) is a continuous quantity and respective cells of the buffer arrays 1210 and 1230 with respect to s_(i) ^(z) are configured with multi-bits and are assumed as a pseudo-continuous quantity.

Effect of the temperature in the present invention is estimated as follows. Bit manipulation is performed in the LED (LD) array 1110, the polarized wave modulator 1112, and the buffer arrays 1210 and 1230. A voltage needed for bit inversion is about 1V. If it is assumed that e is the elementary charge and k_(B) is the Boltzmann constant, a conversion temperature is T=˜1.2×10⁴K by T=eV/k_(B). This value is sufficiently larger than a room temperature of 300K, an influence of the temperature can be ignored in the configuration as in Examples 9 to 12, and it can be operated in the room temperature.

The present invention is not limited to the embodiments described above and includes various modifications. For example, it is possible to replace a portion of a configuration of an example with a configuration of another example and also, it is possible to add a configuration of another example to a configuration of a certain example. Also, it is possible to add, delete, and replace of a configuration of another example, with respect to a portion of a configuration of a certain example.

INDUSTRIAL APPLICABILITY

For example, it is available to a field of a computer for dealing with a problem to be solved that needs exhaustive search.

REFERENCE SIGNS LIST

-   -   100 procedure     -   201 main storage device     -   202 computing device     -   203 control device     -   204 auxiliary storage device     -   205 input device     -   206 output device     -   1000 local field response computing device     -   1100 control unit     -   1110 LED (LD) array     -   1111 LD     -   1112 polarized wave modulator     -   1113 polarization separator     -   1115 demultiplexer     -   1120 variable mask (variable attenuator)     -   1125 multiplexer     -   1130 detector array     -   1131 differential amplifier     -   1200 control unit     -   1210 buffer array     -   1220 computing unit     -   1221 memory     -   1230 buffer array 

1. A computer that includes a computing unit, a storing unit, and a control unit and performs computation while exchanging data between the storing unit and the computing unit by control of the control unit, wherein N variables s_(j) ^(z) (j=1, 2, . . . , N) take a range of −1≦s_(j) ^(z)≦1 and a problem to be solved is set using a local field g_(j) and intervariable interaction J_(ij) (i, j=1, 2, . . . , N), in the computing unit, computation is discretely performed from t=t₀ (t₀=0) to t_(m) (t_(m)=τ) by dividing time into m timepieces, B_(j) ^(z)(t_(k))={Σ_(i)J_(ij)s_(i) ^(z)(t_(k−1))+g_(j)+sgn(s_(j) ^(z)(t_(k−1)))·g_(pina)}·t_(k)/τ or B_(j) ^(z)(t_(k))={Σ_(i)J_(ij)s_(i) ^(z)(t_(k−1))+g_(j)+g_(pinb)·s_(j) ^(z)(t_(k−1))}·t_(k)/τ is obtained by using a value of a variable s_(i) ^(z)(t_(k−1)) (i=1, 2, . . . , N) of previous time t_(k−1) and a coefficient g_(pina) or g_(pinb) of a relaxation term each time when the variable s_(j) ^(z)(t_(k)) is obtained at each time t_(k), and a function f is determined so as to cause the range of the variable s (t_(k)) to become −1≦s_(j) ^(z)(t_(k))≦1 and results in s_(j) ^(z)(t_(k))=f(B_(j) ^(z)(t_(k)), t_(k)), the variable s_(j) ^(z) is caused to approach −1 or 1 by making a time step advance from t=t₀ to t=t_(m), and finally determines a solution in such a way that if s_(j) ^(z)<0, then s_(j) ^(zd)=−1 and otherwise, if s_(j) ^(z)>0, then s_(j) ^(zd)=1.
 2. The computer according to claim 1, wherein regarding the function f, it is assumed that B_(j) ^(x)(t_(k))=γ(1−t_(k)/τ) using a certain constant γ, θ is defined by tan θ=B_(j) ^(z)(t_(k))/B_(j) ^(x)(t_(k)), the s_(j) ^(z)(t_(k)) is determined by s_(j) ^(z)(t_(k))=sin θ, and accordingly, the function f becomes f(B_(j) ^(z)(t_(k)),t_(k))=sin(arctan(B_(j) ^(z)(t_(k))/B_(j) ^(x)(t_(k)))).
 3. The computer according to claim 1, wherein correction parameters r_(s) and r_(B) are added to the function f, θ is defined by tan θ=r_(B)·B_(j) ^(z)(t_(k))/B_(j) ^(x)(t_(k)), the s_(j) ^(z)(t_(k)) is determined by s_(j) ^(z)(t_(k))=r_(s)·sin θ, and accordingly, the function f becomes f(B_(j) ^(z)(t_(k)),t_(k))=r_(s)·sin(arctan(r_(B)·B_(j) ^(z)(t_(k))/B_(j) ^(x)(t_(k)))).
 4. The computer according to claim 3, wherein the correction parameters r_(s) and r_(B) are caused to be depended on the t_(k) and B_(j) ^(z)(t_(k)).
 5. The computer according to claim 1, wherein by assuming that at each time t_(k), if s_(j) ^(z)(t_(k))<0, then s_(j) ^(zd)(t_(k))=−1, and otherwise, if s_(j) ^(z)(t_(k))>0, then s_(j) ^(zd)(t_(k))=1, H_(p)(t_(k))=−Σ_(i>j)J_(ij)s_(i) ^(zd)(t_(k))s_(j) ^(zd)(t_(k))−Σ_(j)g_(j)s_(j) ^(zd)(t_(k)) is calculated at each time t_(k) and s_(j) ^(zd)(t_(k′)) at each time t_(k′), at which H_(p)(t_(k)) became the minimum value, is assumed as a final solution.
 6. The computer according to claim 1, wherein the coefficient g_(pina) is a value from 1% to 50% of an average value of |J_(ij)|.
 7. The computer according to claim 1, wherein regarding a local field g_(j) (j=1, 2, . . . , N) for setting of the problem to be solved, if a component of g_(j)≠0 is present, the B_(j) ^(z)(t_(k)) described above is determined by B_(j) ^(z)(t_(k))={Σ_(i)J_(ij)s_(i) ^(z)(t_(k−1))+g_(j)}·t_(k)/τ, and if g_(j)=0 in all of N, the B_(j) ^(z)(t_(k)) described above is determined by B_(j) ^(z)(t_(k))={Σ_(i)J_(ij)s_(i) ^(z)(t_(k−1))+sgn(s_(j) ^(z)(t_(k−1)))·g_(pina)}·t_(k)/τ or B_(j) ^(z)(t_(k))={Σ_(i)J_(ij)s_(i) ^(z)(t_(k−1))+g_(j)+g_(pinb)·s_(j) ^(z)(t_(k−1))}·t_(k)/τ.
 8. A computing program that is executed by a computing unit, wherein N variables s_(j) ^(z) (j=1, 2, . . . , N) take a range of −1≦s_(j) ^(z)≦1 and a problem to be solved is set using a local field g_(j) and intervariable interaction J_(ij) (i, j=1, 2, . . . , N), computation is discretely performed from t=t₀ (t₀=0) to t_(m) (t_(m)=τ) by dividing time into m timepieces, B_(j) ^(z)(t_(k))={Σ_(i)J_(ij)s_(i) ^(z)(t_(k−1))+g_(j)+sgn(s_(j) ^(z)(t_(k−1)))·g_(pina)}·t_(k)/τ or B_(j) ^(z)(t_(k))={Σ_(i)J_(ij)s_(i) ^(z)(t_(k−1))+g_(j)+g_(pinb)·s_(j) ^(z)(t_(k−1))}·t_(k)/τ is obtained by using a value of a variable s_(i) ^(z)(t_(k−1)) (i=1, 2, . . . , N) of previous time t_(k−1) and a coefficient g_(pina) or g_(pinb) of a relaxation term each time when the variable s_(j) ^(z)(t_(k)) is obtained at each time t_(k), and a function f is determined so as to cause the range of the variable s_(j) ^(z)(t_(k)) to become −1≦s_(j) ^(z)(t_(k))≦1 and results in s_(j) ^(z)(t_(k))=f(B_(j) ^(z)(t_(k)), t_(k)), the variable s_(j) ^(z) is caused to approach −1 or 1 by making a time step advance from t=t₀ to t=t_(m), and finally determines a solution in such a way that if s_(j) ^(z)<0, then s_(j) ^(zd)=−1 and otherwise, if s_(j) ^(z)>0, then s_(j) ^(zd)=1.
 9. The computing program according to claim 8, wherein regarding the function f, it is assumed that B_(j) ^(x)(t_(k))=γ(1−t_(k)/τ) using a certain constant γ, θ is defined by tan θ=B_(j) ^(z)(t_(k))/B_(j) ^(x)(t_(k)), the s_(j) ^(z)(t_(k)) is determined by s_(j) ^(z)(t_(k))=sin θ, and accordingly, the function f becomes f(B_(j) ^(z)(t_(k)),t_(k))=sin(arctan(B_(j) ^(z)(t_(k))/B_(j) ^(x)(t_(k)))).
 10. The computing program according to claim 8, wherein correction parameters r_(s) and r_(B) are added to the function f, θ is defined by tan θ=r_(B)·B_(j) ^(z)(t_(k))/B_(j) ^(x)(t_(k)), the s_(j) ^(z)(t_(k)) is determined by s_(j) ^(z)(t_(k))=r_(s)·sin θ, and accordingly, the function f becomes f(B_(j) ^(z)(t_(k)),t_(k))=r_(s)·sin(arctan(r_(B)·B_(j) ^(z)(t_(k))/B_(j) ^(x)(t_(k)))).
 11. The computing program according to claim 10, wherein the correction parameters r_(s) and r_(B) are caused to be depended on the t_(k) and B_(j) ^(z)(t_(k)).
 12. The computing program according to claim 8, wherein by assuming that at each time t_(k), if s_(j) ^(z)(t_(k))<0, then s_(j) ^(zd)(t_(k))=−1, and otherwise, if s_(j) ^(z)(t_(k))>0, then s_(j) ^(zd)(t_(k))=1, H_(p)(t_(k))=−Σ_(i>j)J_(ij)s_(i) ^(zd)(t_(k))s_(j) ^(zd)(t_(k))−Σ_(j)g_(j)s_(j) ^(zd)(t_(k)) is calculated at each time t_(k) and s_(j) ^(zd)(t_(k′)) at each time t_(k′), at which H_(p)(t_(k)) became the minimum value, is assumed as a final solution.
 13. The computing program according to claim 8, wherein the coefficient g_(pina) is a value from 1% to 50% of an average value of |J_(ij)|.
 14. The computing program according to claim 8, wherein regarding a local field g_(j) (i, j=1, 2, . . . , N) for setting of the problem to be solved, if a component of g_(j)≠0 is present, the B_(j) ^(z)(t_(k)) described above is determined by B_(j) ^(z)(t_(k))={Σ_(i)J_(ij)s_(i) ^(z)(t_(k−1))+g_(j)}·t_(k)/τ, and if g_(j)=0 in all of N, the B_(j) ^(z)(t_(k)) described above is determined by B_(j) ^(z)(t_(k))={Σ_(i)J_(ij)s_(i) ^(z)(t_(k−1))+sgn(s_(j) ^(z)(t_(k−1)))·g_(pina)}·t_(k)/τ or B_(j) ^(z)(t_(k))={Σ_(i)J_(ij)s_(i) ^(z)(t_(k−1))+g_(j)+g_(pinb)·s_(j) ^(z)(t_(k−1))}·t_(k)/τ.
 15. A computer that includes a computing unit, a storing unit, and a control unit and performs computation while exchanging data between the storing unit and the computing unit by control of the control unit, the computer comprising: a local field response computing device, wherein in the local field response computing device, N variables s_(j) ^(z) (j=1, 2, . . . , N) take a range of −1≦s_(j) ^(z)≦1 and a problem to be solved is set using a local field g_(j) and intervariable interaction J_(ij) (i, j=1, 2, . . . , N), in the computing unit, computation is discretely performed from t=t₀ (t₀=0) to t_(m) (t_(m)=τ) by dividing time into m timepieces, B_(j) ^(z)(t_(k))={Σ_(i)J_(ij)s_(i) ^(z)(t_(k−1))+g_(j)+sgn(s_(j) ^(z)(t_(k−1)))·g_(pina)}·t_(k)/τ or B_(j) ^(z)(t_(k))={Σ_(i)J_(ij)s_(i) ^(z)(t_(k−1))+g_(j)+g_(pinb)·s_(j) ^(z)(t_(k−1))}·t_(k)/τ is obtained by using a value of a variable s_(i) ^(z)(t_(k−1)) (i=1, 2, . . . , N) of previous time t_(k−1) and a coefficient g_(pina) or g_(pinb) of a relaxation term each time when the variable s_(j) ^(z)(t_(k)) is obtained at each time t_(k), and a function f is determined so as to cause the range of the variable s_(j) ^(z)(t_(k)) to become −1≦s_(j) ^(z)(t_(k))≦1 and results in s_(j) ^(z)(t_(k))=f(B_(j) ^(z)(t_(k)), t_(k)), the variable s_(j) ^(z) is caused to approach −1 or 1 by making a time step advance from t=t₀ to t=t_(m), finally determines a solution in such a way that if s_(j) ^(z)<0, then s_(j) ^(zd)=−1 and otherwise, if s_(j) ^(z)>0, then s_(j) ^(zd)=1, and processing in the local field response computing device is performed by using modulation of a plurality of optical signals parallely processed in an optical system or by parallely processing data accumulated in a buffer array. 